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RS-JEPA framework improves cross-sensor remote sensing image retrieval

By PulseAugur Editorial · [1 sources] ·

Researchers have developed RS-JEPA, a novel self-supervised learning framework for cross-sensor remote sensing image retrieval. This method utilizes a Vision Mamba encoder and introduces "scan-path views" to predict hidden image patches from visible ones, enhancing retrieval accuracy. RS-JEPA achieves state-of-the-art performance on multiple benchmarks with significantly fewer parameters compared to previous approaches. AI

IMPACT This research advances self-supervised learning techniques for image retrieval, potentially improving applications in remote sensing and cross-sensor data analysis.

RANK_REASON The cluster contains an academic paper detailing a new model architecture and methodology. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

RS-JEPA framework improves cross-sensor remote sensing image retrieval

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36 / 100
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The cluster contains an academic paper detailing a new model architecture and methodology. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Tanishq Rachamalla, Srishti Kaushik, Muhammad Attique Khan, Gustau Camps-Valls, Swalpa Kumar Roy ·

    RS-JEPA: Remote Sensing Joint-Embedding Predictive Architecture with Scan-Path Views for Cross-Sensor Image Retrieval

    arXiv:2611.14496v1 Announce Type: new Abstract: Cross-sensor remote sensing image retrieval aims to find scenes with the same content across heterogeneous sensors, such as SAR and optical or panchromatic (PAN) and multispectral (MS) imagery, without manual labels. Existing self-s…

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